Human Activity Classification and Identification Using Structural Vibrations

ABSTRACT

The current disclosure is directed to classifying/identifying an action based on the vibrations caused in/on/around a structure by the action and providing a confidence level association between the vibration and the action that caused the vibration.

BACKGROUND OF THE INVENTION 1) Field of the Invention

The present invention relates to sensing and categorizing impacts on astructure to determine what impacted the structure, i.e., classifyingand/or identifying an action based on the vibrations caused in astructure by the action.

2) Description of Related Art

Numerous references disclose impact analysis to determine sources ofimpact, the type of material impacted, etc. For example, U.S. Pat. No.3,759,085 discloses an impact sensor and coder apparatus for use in amaterials-sorting system. A movable rigid body is adapted to strikeindividual pieces of the materials. An accelerometer associated with therigid body measures the rate of deceleration of the rigid body, as afunction of time, in terms of a voltage signal waveform. The waveformthus derived is compared with a group of typical waveforms to determinewhich of the waveforms of the group conforms most closely to that of thesample, thereby to identify the sample. (Abstract.) '085 attempts todetermine the type of material being struck.

US 20170301207 discloses an impact detection methodology. Systems andmethods can be utilized to detect impacts of concern such as collisions,falls, or other incidents. Systems and methods can be utilized tomonitor an area and detect falls or collisions of an individual, forinstance, as may require intervention to aid the subject. A system caninclude two or more accelerometers and a controller. The accelerometerscan be in communication with the structure (e.g., within or on the wallsor floor of a structure) and can monitor the structure for vibrations.The accelerometers can be coupled to a controller that is configured toprocess data obtained from the accelerometers and provide output withregard to the force and/or location of an impact within the structure.(Abstract.) The current disclosure differs significantly by firstdecomposing a vibration signal into various constants/parts and then adeep learning machine, machine learning classifier, or artificialintelligence algorithm is used to identify the action that caused thevibration rather than the force or location. In a sense, the '207reference informs one of where and how hard something is touching you(e.g. on your arm with light pressure) and the current disclosure tellswhat it is touching you (e.g. pencil).

U.S. Pat. No. 9,827,935 discloses an apparatus that includes a centercomponent defining a center chamber therein and first and second sidecomponents defining first and second chambers therein, respectively. Thefirst and second side components are coupled to opposing ends of thecenter component with the first and second chambers in fluidcommunication with the center chamber. The center, first side and secondside components are configured to extend substantially across a width ofa vehicle. The apparatus further includes first, second and thirdpressure sensors in communication with the first, second and centerchambers, respectively. (Abstract.) The current disclosure does notemploy impact sensors.

U.S. Pat. No. 9,364,748 discloses an example system and method fordetecting a moment of impact and/or strength of a swing based on movinga hand-held device including an accelerometer arrangement. A moment anda magnitude of simulated striking of the object are determined based onone or more accelerometer arrangement outputs resulting from the movingof the hand-held device. Using one or more of aural, visual and tactileoutputs, the striking of the object is simulated in accordance with thedetermined moment of simulated striking and the determined magnitude ofthe simulated striking. (Abstract.) This disclosure does not identifyimpacts on a structure, nor identifying characteristics based on thisidentification.

U.S. Pat. No. 4,870,868 discloses a sensing device, which produces aresponse when the point of impact between an object and a member occursat a preselected location on the member. When the member vibrates afterbeing impacted by the object, an oscillatory electrical signal isproduced by a piezoelectric sensor. Appropriate circuitry is providedfor analyzing the oscillatory electrical signal and for producing aresponse if the object impacted the member at the preselected location.The sensing apparatus is particularly useful in athletics fordetermining whether a game object contacted the athletic instrument atits “sweet spot”. (Abstract.) This disclosure does not employlocalization techniques as explained in the current disclosure.

U.S. Pat. No. 9,489,777 discloses a device for detecting the impact ofan object on a vehicle, which comprises a hose filled with a fillingmedium. The device also comprises a first pressure sensor which isconnected with a first end of the hose and a second pressure sensorconnected with a second end of the hose. An electronic control unit isconnected with the first and the second pressure sensor and is designedfor the processing of the signals received by the first and the secondpressure sensor. The hose is sealed off with respect to the environmentand the first and the second pressure sensor, so that the internalpressure of the filling medium is independent of an ambient pressure.The electronic control unit is designed for processingtemperature-caused changes of the internal pressure as a criterion forthe diagnosis of the operability of the device. (Abstract.) Thisdisclosure does not identify impacts on a structure, nor identifyingcharacteristics based on this identification.

U.S. Pat. No. 8,948,961 discloses a method and an apparatus fordetecting a pedestrian impact, at least three acceleration sensors beingprovided which are respectively mounted on the inner side of the bumpercladding and each generate a signal. The pedestrian impact is detectedas a function of a time offset between at least two of the threesignals. The impact location is identified on the basis of the at leastone time offset. (Abstract.) The current disclosure does not employ atime delay analysis.

U.S. Pat. No. 8,577,555 discloses an impact detection system with twochambers disposed adjacent to one another. The two chambers haveopposing tapered shapes, so that an impact anywhere along them willcreate a different pressure wave or pulse in each chamber. A pressuresensor module incorporating two pressure sensors is disposed at one endof the dual-channel unit, and comparison of the signals from the sensorscan be used to discriminate both the location and severity of apedestrian impact. (Abstract.) This disclosure uses pressure sensors anda tapered design to locate impacts; the current disclosure does neither.

US 2017/0096117 discloses a method for determining an impact location ofan object on a vehicle including reading in a first sensor signal valueof a first sensor of the vehicle at a predefined first point in time, asecond sensor signal value of the first sensor at a predefined secondpoint in time following the first point in time, and a sample value of asecond sensor of the vehicle at a third point in time following thesecond point in time. Additionally, an interpolation point is calculatedfrom the first sensor signal value and the second sensor signal value byusing the sample value, at least one component of the interpolationpoint corresponding to the sample value. A time lag between aninterpolation instant assigned to the interpolation point, and the thirdpoint in time takes place. Finally, the time lag is used for determiningthe impact location of the object. (Abstract.) The current disclosuredoes not employ time lag analysis.

CN106482638 relates to an electrical invention analyzing amplitudes andsignal energies for electrical component analysis. This disclosure doesnot identify impacts on a structure, nor identifying characteristicsbased on this identification.

U.S. Pat. No. 9,453,759 discloses a system for determining vibrationcharacteristics of a motor vehicle having a sensing arrangement adaptedto sense vibrations of the vehicle or a vehicle part, and an electronicprocessing means adapted to apply an algorithm for evaluating signalsfrom the sensing arrangement and for determining vibrationcharacteristics based on the evaluation. The algorithm includes at leastone support vector machine SVM (13a . . . 13g) adapted to output aprobability that the current vibration characteristic belongs to aparticular pre-set type of vibration characteristic. (Abstract.) Thisdisclosure does not identify impacts on a structure, nor identifyingcharacteristics based on this identification.

US 20150377694 discloses systems and methods for remotely detecting andassessing collision impacts using one or more acoustical sensors, suchas using acoustical sensors to detect helmet collisions on an athleticplaying field. For example, at least one acoustical sensor is disposedadjacent an athletic playing field and remotely from the one or moreplayers on the athletic playing field. A processor of a computing devicein communication with the acoustical sensor is configured foridentifying whether the acoustical signal indicates a collision eventoccurred between a helmet and another object. The processor may also beconfigured for identifying a location on the playing field where thecollision event occurred and/or identifying one or more characteristicsof the acoustical signal to determine the amount of force, the duration,the speed, the acceleration, and/or the location of the collision eventon the helmet. (Abstract.) The current disclosure does not employacoustic analysis.

U.S. Pat. No. 7,430,914 discloses a vibration analyzing device fordetermining the vibrational response of a structural element,comprising: a vibration sensor for providing an output in response to aforce input imparted to the structural element; processing means adaptedto determine one of a plurality of classifications in response to theoutput, each classification corresponding to a condition of thestructural element; and display means for displaying the determinedclassification. (Abstract.) This disclosure does not identify impacts ona structure, nor identifying characteristics based on thisidentification.

US 20180018509 discloses an indoor person identification system thatutilizes the capture and analysis of footstep induced structuralvibrations. The system senses floor vibration and detects the signalinduced by footsteps. Then the system then extracts features from thesignal that represent characteristics of each person's unique gaitpattern. With these extracted features, the system conducts hierarchicalclassification at an individual step level and at a collection ofconsecutive steps level, achieving high degree of accuracy in theidentification of individuals. (Abstract.) This disclosure does notidentify impacts on a structure, nor identifying characteristics basedon this identification. Further, the '509 reference they have to amplifythe signal in order to obtain better results, we do not needamplification Accordingly, it is an object of the present invention tosense, analyze, and categorize what caused impacts on a structure inorder to categorize what impacted the structure.

BRIEF DESCRIPTION OF THE DRAWINGS

The construction designed to carry out the invention will hereinafter bedescribed, together with other features thereof. The invention will bemore readily understood from a reading of the following specificationand by reference to the accompanying drawings forming a part thereof,wherein an example of the invention is shown and wherein:

FIG. 1 shows an outline of one method of the current disclosure.

FIG. 2 shows a two Cascading Decision Layer (CDL) example of the currentdisclosure.

FIG. 3 shows one embodiment of an experimental layout of the currentdisclosure.

FIG. 4 shows Table 1, which displays a list of activity types for theexperimental example.

FIG. 5 shows Table 2, which shows a classification and identification oftypes for the experimental example.

FIG. 6 shows Table 3, which shows Support Vector Machine (SVM) Top 22Classification Metric Combination Scores (Layer One).

FIG. 7 shows Table 4, which shows SVM Top 22 Identification MetricCombination Scores (Layer Two).

FIG. 8 shows Table 5, which shows Multi-Logistic Regression (MLR) Top 22Classification Metric Combination Scores (Layer One).

FIG. 9 shows Table 6, which shows MLR Top 22 Identification MetricCombination Scores (Layer Two).

FIG. 10 shows metric sensitivity of various activities.

FIG. 11 shows Table 7, which shows SVM Top 20 Classification MetricCombination Scores for Layer One Assuming an Unknown Location.

FIG. 12 shows Table 8, which shows SVM Top 20 Classification MetricCombinations Scores for Layer Two Assuming an Unknown Location.

FIG. 13 shows Table 9, which shows MLR Top 20 Classification MetricCombination Scores for Layer One Assuming an Unknown Location.

FIG. 14 shows Table 10, which shows MLR Top 20 Identification MetricCombination Scores Layer Two Assuming an Unknown Location.

FIG. 15 shows Table 11, which shows SVM Top 9 Classification MetricCombination Scores for Layer One Assuming an Unknown Location andSensor.

FIG. 16 shows Table 12, which shows SVM Top 20 Classification MetricCombination Scores for Layer Two Assuming an Unknown Location andSensor.

FIG. 17 shows Table 13, which shows MLR Top 4 Classification MetricCombination Scores for Layer One Assuming an Unknown Location andSensor.

FIG. 18 shows Table 14, which shows MLR Top 16 Identification MetricCombination Scores Layer Two Assuming an Unknown Location.

FIG. 19 shows Table 15, which shows MLR Results Using NormalizedAutocorrection (NA) (known location).

FIG. 20 shows Table 16, which shows SVM Results Using NA (knownlocation).

FIG. 21 shows Table 17, which shows MLR Results Using NA (unknownlocation).

FIG. 22 shows Table 18, which shows SVM Results Using NA (unknownlocation).

FIG. 23 shows Table 19, which shows MLR Results Using NA (unknownlocation and sensor).

FIG. 24 shows Table 20, which shows SVM Results Using NA (unknownlocation and sensor).

FIG. 25 shows Sensor 1's noise removal.

FIG. 26 shows Sensor 2's noise removal.

FIG. 27 shows Sensor 3's noise removal.

It will be understood by those skilled in the art that one or moreaspects of this invention can meet certain objectives, while one or moreother aspects can meet certain other objectives. Each objective may notapply equally, in all its respects, to every aspect of this invention.As such, the preceding objects can be viewed in the alternative withrespect to any one aspect of this invention. These and other objects andfeatures of the invention will become more fully apparent when thefollowing detailed description is read in conjunction with theaccompanying figures and examples. However, it is to be understood thatboth the foregoing summary of the invention and the following detaileddescription are of a preferred embodiment and not restrictive of theinvention or other alternate embodiments of the invention. Inparticular, while the invention is described herein with reference to anumber of specific embodiments, it will be appreciated that thedescription is illustrative of the invention and is not constructed aslimiting of the invention. Various modifications and applications mayoccur to those who are skilled in the art, without departing from thespirit and the scope of the invention, as described by the appendedclaims Likewise, other objects, features, benefits and advantages of thepresent invention will be apparent from this summary and certainembodiments described below, and will be readily apparent to thoseskilled in the art. Such objects, features, benefits and advantages willbe apparent from the above in conjunction with the accompanyingexamples, data, figures and all reasonable inferences to be drawntherefrom, alone or with consideration of the references incorporatedherein.

SUMMARY OF THE INVENTION

In one embodiment the current disclosure provides a system forcategorizing actions based on vibrations. The system may include atleast one sensor, at least one data collector that receives informationfrom the at least one sensor, a processor analyzing a vibration signalobtained from the information received from the at least one sensor,wherein the processor determines time domain components and/or frequencydomain components of the vibration signal, and a classifier that employsat least one algorithm to analyze the time domain components and/orfrequency domain components of the vibration signal, wherein theclassifier associates the time domain components and/or frequency domaincomponents of the vibration signal with a known action. Further, theknown action may comprises a form of human movement. Still further, theknown action may comprise a form of movement other than human movement.Yet again, the at least one sensor may comprise an accelerometer. Stillfurther, the time domain component may comprise maximum amplitude,zero-crossing rate, and/or duration and/or other time domain componentsknown or known in the future to the practice. Yet still, the frequencydomain components may comprise Fourier transform, discrete cosinetransform, and/or power spectral density and/or other frequency domaincomponents known or known in the future to the practice. Further, theclassifier may provide a probability assessment that correlates thevibration signal to a known action within a predefined confidence level.Yet further, at least one machine learning or artificial intelligencemay be employed to associate the time domain components and/or frequencydomain components of the vibration signal with the known action. Furtheragain, undesirable sound or vibration components may be removed from thevibration signal prior to the at least one algorithm being used toanalyze the time domain components and/or frequency domain components ofthe vibration signal.

In an alternative embodiment, the disclosure provides a method forcategorizing actions based on vibrations. The method may includedetecting at least one vibration, converting the at least one vibrationinto information, obtaining at least one vibration signal from theinformation, determining time domain components and/or frequency domaincomponents of the vibration signal, analyzing the time domain componentsand/or frequency domain components of the vibration signal, andassociating the time domain components and/or frequency domaincomponents of the vibration signal with a known action. Further, theknown action may comprise a form of human movement. Still yet, the knownaction may comprise a form of movement other than human movement. Yetagain, the at least one sensor may comprise an accelerometer. Furtheragain, the time domain component may comprise maximum amplitude,zero-crossing rate, and/or duration. Furthermore, the frequency domaincomponents may comprise Fourier transform, discrete cosine transform,and/or spectral power density and/or other frequency domain componentsknown or known in the future to the practice. Still yet, the classifiermay provide a probability assessment that correlates the vibrationsignal to a known action within a predefined confidence level. Againfurther, at least one machine learning or artificial intelligence mayassociate the time domain components and/or frequency domain componentsof the vibration signal with the known action. Still further,undesirable sound or vibration components may be removed from thevibration signal prior to the at least one algorithm being used toanalyze the time domain components and/or frequency domain components ofthe vibration signal.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

With reference to the drawings, the invention will now be described inmore detail. Unless defined otherwise, all technical and scientificterms used herein have the same meaning as commonly understood to one ofordinary skill in the art to which the presently disclosed subjectmatter belongs. Although any methods, devices, and materials similar orequivalent to those described herein can be used in the practice ortesting of the presently disclosed subject matter, representativemethods, devices, and materials are herein described.

Unless specifically stated, terms and phrases used in this document, andvariations thereof, unless otherwise expressly stated, should beconstrued as open ended as opposed to limiting. Likewise, a group ofitems linked with the conjunction “and” should not be read as requiringthat each and every one of those items be present in the grouping, butrather should be read as “and/or” unless expressly stated otherwise.Similarly, a group of items linked with the conjunction “or” should notbe read as requiring mutual exclusivity among that group, but rathershould also be read as “and/or” unless expressly stated otherwise.

Furthermore, although items, elements or components of the disclosuremay be described or claimed in the singular, the plural is contemplatedto be within the scope thereof unless limitation to the singular isexplicitly stated. The presence of broadening words and phrases such as“one or more,” “at least,” “but not limited to” or other like phrases insome instances shall not be read to mean that the narrower case isintended or required in instances where such broadening phrases may beabsent.

The current disclosure is directed to classifying an action based on thevibrations caused in a structure by the action. This provides theability to identify an action based on the vibration the action causedin/on/to the structure (e.g., building, vehicle, boat, plane, sidewalk,driveway, tower, etc.) where it occurred. With respect to FIG. 1, in oneembodiment, a vibration analysis 100 may be conducted. At step 102,sensors may be installed anywhere in/on/nearby/around a structure wherea vibration can be felt. Sensors may or may not be in direct contactwith the surface, area, structure or feature from which the sensordetects vibrations. The sensor may be, in one instance, a vibrationmeasuring sensor, including but not limited to a velocity sensor, agyroscope sensor, a pressure or microphone sensor, a laser displacementsensor, a capacitive displacement or Eddy Current sensor, a vibrationmeter, a vibration data logger, or any other known or later developedsensor for measuring vibration. The location or origin of the vibrationmay be known or unknown with respect to this disclosure. At step 104, asignal, e.g., an acceleration or vibration of the structure, aregathered from installed sensors (e.g., LVDT, velocity sensor,displacement sensor, microphone, strain gauge, gyroscope, pressure,capacitive displacement, vibration meter, accelerometer, etc.). Thesensors are connected to a data collector, e.g. data acquisition devicewhether that is a standalone card, other computer of some sort, or otherway to gather data from a sensor, at step 106, the data collector takesdata from the sensors. The data may be sensor agnostic such as, but notbeing limited to, voltage, analog, digital, numeric, strings, dates,times, booleans, arrays, and any other data types/forms known or knownin the future to the practice. At step 108, data is linked to knownactions for initial training of the deep learning, machine learning,and/or artificial intelligence algorithm and the system becomesoperational for analyzing new data. For purposes of example only and notintended to be limiting, a known action may comprise any action where ahuman, non-human (such as animals and/or natural phenomena), or object(such as a ball, wheelchair, cane, walker, chair, etc.) interacts with astructure. This may be a form of human movement, such as a persontalking, humming, playing an instrument, taking footsteps, a personfalling, a person bathing, a person dressing, a person walking, a personeating, a person showering, a person bathing, a person coughing, aperson sneezing, a person toileting, a person changing position from bedto standing, a person moving from sitting to standing, and a personmoving about by any manner a person may move or ambulate including usinga walker, wheelchair, rolator, cane or other device to aid ambulation, aperson squatting, a person jumping, a person exercising, or in otherinstances an object falling, an object rolling across a surface, anobject moving across a surface, etc., or machines vibrating a surface,machines moving along a surface, machines hitting the surface, machinesinteracting with the surface, etc. or an explosion, or still furthernaturally occur phenomenon, an object bouncing along a surface, or stillfurther a pet or animal walking, jumping, falling, etc., or anyhappening that may cause a structure to vibrate, with respect to asurface or feature, as well as combinations of the above. At 110,vibrations/accelerations/movements are again gathered by sensors. At112, a data collector receives the data from the sensors. At step 114,the data collector sends the data for analysis. A data collector may bean Omega Data Acquisition System, Keysight Data Acquisition System, HBMData Acquisition System, National Instruments Data Acquisition System,DATAQ Data Acquisition System, CAS Dataloggers Data Acquisition System,or any data acquisition system known or known in the future to thepractice. A data collector may also be a Lenovo computer, HP computer,Asus computer, Dell computer, Acer computer, server, or other deviceknown or known in the future to the practice, A sensor may bepiezoelectric, micro-electrical mechanical system, velocity sensors,displacement sensors, accelerometers, Memsic sensors, TE ConnectivityVibration sensors, PCB Piezotronics sensors, Mouser vibration sensors,Colibrys vibration sensors, National Instruments vibration sensors, SKFvibration sensors, Bosch vibration sensors PCE Instruments vibrationsensors, or any other vibration sensor known or known in the future tothe practice.

Analytics (e.g. part or all of the deep learning, machine learning,and/or artificial intelligence algorithms operation, preprocessing,postprocessing, event-of-interest detection, etc) may be performed oneither the data collector or some other device, e.g., a server or othercomputer, and sensor signals may be pre-processed with techniques knownor known in the future to the practice (e.g. detrending, frequencyfilters, smoothing filters, peak isolation, blind signal separation,independent component analysis, noise cancelling, normalization,auto-scaling, derivatives, tessellating, curve fitting, windowing,standard deviation, variance, mean, outlier removal). At step 116, theanalytical portion takes time domain components known or known in thefuture to the practice (e.g., amplitude, magnitude, zero-crossing rate,duration, jerk, proportional peak index, threshold-crossing rate, signalenergy, shape, autocorrelation, signal-to-noise ratio, sampling rate,range, autocorrelation, cross correlation, maxima, minima, mean,standard deviation, variance) and/or frequency domain components knownto or known in the future to the practice (e.g. Fourier transform,Fourier series, discrete cosine transform, Laplace transform, Ztransform, wavelet transform, power spectral density, frequencyspectrum, amplitude, magnitude, phase, bandwith, standard deviation,variance, mean, frequency, spectrogram, cross power spectral density,maxima, minima) of an acceleration signal and sends this to a machinelearning algorithm (e.g. support vector machine). At step 116, timedomain components and/or frequency domain components of the signal aretransmitted to a deep learning, machine learning, and/or artificialintelligence algorithm known to the practice such as supervised,semi-supervised, unsupervised, reinforcement-learning algorithms thatmay include Linear Models, Linear and Quadratic Discriminant Analysis,Kernel Ridge Regression, Support Vector Machines, Stochastic GradientDescent, Nearest Neighbors, Gaussian Processes, Naive Bayes, CrossDecomposition, Decision Trees, Ensemble Methods, Neural Networks,Clustering, Association Rules, Q-Learning, Temporal Difference, DeepAdversarial Networks, and/or other algorithms known or known in thefuture to the practice. At step 118, a determination of the cause of thevibration/acceleration/movement signal occurs. To wit, take componentsof the signal in time and/or frequency domain of single or multiplesignals of single or multiple recorded actions. These are fed to thedeep learning, machine learning, and/or artificial intelligencealgorithm with their action linked, if supervised or partially linked ifsemi-supervised, with the resulting signal components so the algorithmlearns what components are linked to what action. At step 120, themachine learning portion returns a probability assessment that thepreviously trained actions were what caused the vibration. If using amachine learning algorithm that is unsupervised is employed, vibrationswill be placed into groups of similar signal components by theunsupervised algorithm without any prior labeling. These groups canlater be connected to actions by feeding signal components of a knownaction and seeing where the algorithm groups the known action, or simplyknowing which of the unlabeled vibrations belongs to which action. Thesignals can be grouped as their components may be similar but thealgorithm would not be able to label the action on its own. The actioncould be later “labeled” by introducing known action's signals at thealgorithm and seeing what group of signals it is put with and then labelthe action that way. The result can be post-processed with methods knownor known in the future to the practice (e.g. providing pre-definedconfidence levels, ignoring results that do not meet a certain criteria,etc.), probability cutoff point, outlier removal). This provides thepercent chance (aka confidence) that the identified action is actuallythe action, sets a threshold for confidence for the result to beconsidered the result or otherwise reports “unknown”, outlier removal,and may apply another additional deep learning, machine learning, orartificial intelligence layers to further refine results. At step 122,the results may be relayed to a user through an API, a readout/display,or other ways to provide output known or will be known to the practice.

Systems of the current disclosure may use one or more rounds ofanalysis, each round may use the same classifier (e.g. deep learning,machine learning, artificial intelligence algorithm), differentclassifier, or any combination thereof to analyze the vibration(s) anddetermine the action(s) that caused it. Each round can be used todetermine different classifications (e.g. categorizations) of thevibrations.

A layering technique entitled Cascading Decision Layers (CDL), was bornto help facilitate differentiation of vibration events. Each layer has agoal, and each subsequent layer using the analysis of the previous tofurther narrow the scope of the event potentiality. A rating, based inprobabilistic outputs from a decision engine, is used to indicate theevent type options the following layer should use. This is similar inapproach to how a SVM classifier decides between classifications,however, SVMs use one-to-one comparisons. CDL in contrast uses aone-to-selected-many method where an event's features are compared tomultiple possible events. With each layer, a probability is generated sostatistical overall ratings of likelihood can be developed to give to auser what the chance is the CDL thinks the event has been identifiedcorrectly.

One may have any number of layers depending on the amount of refinementneeded. The important thing to remember is to give each layer anobtainable goal. For example, two layers are used in the researchpresented here. The first layer's goal is to classify a vibration eventfrom a large array of options into a group. The second layer then isused to further refine the classification so the identification of amore specific action can occur based on the results of the first layer.Considering there are only three classifications and either two or threepossible identification types per classification, this is a reasonableamount of layers. In one embodiment, classifications may be chosen bythe user and do not have a methodology besides classification in a groupor general term (e.g., ball, car) and identification is the specific(e.g., basketball, sedan). Generally, similar objects or actions wouldbe classified together if they are alike physically or have similarimpact vibration patterns. Then the more specific label within the groupwould be the identification. CDL is a method for layering deep learning,machine learning, and/or artificial intelligence algorithms so it can bean unlimited number of layers. This is demonstrated in FIG. 2, whichdemonstrates a two CDL example 200. To initiate first layer analysis201, vibration data 200 (or movement or acceleration data, etc.) isintroduced to first layer 204 via providing vibration data 200 to aclassifier 204 a, which in turn generates ratings 206. Ratings 206 areused to select an event class 208 and in turn generate a classifiedevent 210 wherein vibration data 200 is narrowed into a smaller group ofpotential events. The selection of an event class by ratings can be doneusing any methods known or known in the future to the practice,including but not limited to, threshold cutoff where a defined rating isused to choose the classes to continue or simply taking the top tworated classes. To initiate second layer analysis 212, classified event210, which may in the form of data, is fed again into classifier 204 b,which may be an entirely different classifier, if 204 b is a differentclassifier, the second classifier (deep learning, machine learning, orartificial intelligence algorithm) can be either the same type ofclassifier (e.g. SVM, MLR) that was trained using only a limited set ofdata (chosen based on results of the first layer) or a different (eitherusing a subset of training data based on the previous layer or the fullset). This in turn generates ratings 214, which consists of percentconfidence that the event is one of the identified options. Ratings 214are then used to choose event identity 216, which in turn producesidentified event 218. In the last layer, the highest scoring evenidentity is typically taken as the identified event, though othertechniques known to the practice may be used including reportingconfidence to the user.

The duration of an event is defined to be the time it takes for thesignal to return to rest conditions. In an application scenario, thesignal's rest conditions are defined to be when the sensor is readingdata below a threshold level. This is due to the fact that the sensoritself cannot physically read a zero value. Equation 1, below,calculates the duration of the event where N is the number of points inthe signal window, Π( ) is the indicator function where its value is oneif the condition is true and zero if not, n is the location within thesignal, S is the signal, T is the threshold level, and fs is thefrequency of sampling used to capture the signal. Force was calculatedusing the Force Estimation and Event Localization (FEEL(algorithm.

$\begin{matrix}{D = \frac{\sum\limits_{n = 0}^{N}\; {{II}\left( {S_{n} > T} \right)}}{f_{s}}} & (1)\end{matrix}$

A simple feature of signal is its maximum amplitude. The thinking beingthat the maximum amplitude may be of use to help differentiate betweentypes of a categorization groups. In other words, it stands to reasonthat the amplitude of an object dropped from a high elevation will belarger than that of an object dropped from a low elevation simplybecause there is more energy when the object impacts. Equation 2calculates the maximum amplitude where max( ) is the maximum valuefunction, and S is the signal.

A _(max)=max(|S|)   (2)

Jerk is the rate of change of acceleration. Here the maximum value ofthe jerk vector is taken in Equation 3 where max( ) is the maximum valuefunction, and S(t) is the acceleration signal with respect to time t. Bybeing a direct derivative of the acceleration signal, information isdirectly embedded in the resulting vector that is potentially useful forhuman activity recognition.

$\begin{matrix}{{MJ} = {\max \left( \frac{{dS}(t)}{dt} \right)}} & (3)\end{matrix}$

Signal Energy, in a signal processing sense, describes the amount ofactivity present in a signal. Multiple actions may result in similarenergy values, however, this metric has been used in past research todifferentiate human activity types and thus was included forthoroughness. See Yaniv Zigel, Dima Litvak, and Israel Gannot. “A Methodfor Automatic Fall Detection of Elderly People using Floor Vibrationsand Sound-Proof of Concept on Human Mimicking Doll Falls”. In: IEEETransactions on Biomedical Engineering (2009), which is herebyincorporated by reference.

The sum of squares was used to calculate signal energy as seen inEquation 4 where N is the number of points in the signal window, andS(t) is the acceleration signal with respect to time t.

E _(s)=∫₀ ^(N) |S(t)|² dt   (4)

Zero Crossing Rate (ZCR) measures the rate a signal crosses the zerothreshold (i.e. sign changes) and has been used extensively in speechrecognition. The zero crossing rate was included as seen in Equation 5where N is the number of points in the signal, M( ) is the indicatorfunction where its value is one if the condition is true and zero ifnot, S is the signal itself, and n is the position within the signal.

$\begin{matrix}{{ZCR} = {\frac{1}{N - 1}{\sum\limits_{n = 1}^{N - 1}\; {{II}\left( {{S_{n}S_{n - 1}} < 0} \right)}}}} & (5)\end{matrix}$

Proportional Peak Time Index (PPTI), see below at Equation 6, createsproportions to the maximum amplitude within a signal (refer to 0054).The integral of the proportion curve generated by using the followingequation where a is ratio between 0 and 1 of the maximum amplitude, andη( ) is the fall time defined as the time between the maximum amplitudeand the last sample that is above a times the amplitude. Refer toMadarshahian, Ramin, and Juan M. Caicedo. “Human Activity RecognitionUsing Multinomial Logistic Regression.” Model Validation and UncertaintyQuantification, Volume 3. Springer, Cham, 2015. 363-372. for moreinformation.

PPTI=∫_(α=0) ^(α=1)η(α)dα  (6)

Experimental Setup. This research used the Human Activity Benchmarkdataset provided by the University of South Carolina's StructuralDynamics and Intelligent Infrastructure (SDII) Laboratory, and developedby Diego Arocha. See, Diego Arocha. “Time Domain Methods to StudyHuman-Structure Interaction”. Master of Science. University of SouthCarolina, 2013, which is hereby incorporated by reference along withMadarshahian, Ramin, Juan M. Caicedo, and Diego Arocha Zambrana.“Benchmark problem for human activity identification using floorvibrations.” Expert Systems with Applications 62 (2016): 263-272. whichis hereby incorporated by reference.

The experiments were performed in the second story office of theUniversity of South Carolina's (USC) Structures Laboratory, measuring777 cm (25.5 ft) by 638 cm (20.9 ft), that has reinforced concretefloors covered in vinyl tiles. Sensors with sensitivity of 1000 mV/gwere installed, with three being on the floor near the walls and onebeing in the center of the room. The sensors were connected to a dataacquisition card, such as a NI CompactDAQ with a NI9234 module. Data wascollected at a rate of 1651.7 Hz with 2 s windows. FIG. 3 shows theConcrete Floor Experimental Layout.

A total of 120 records are available for each activity type, describedin Table 1, see FIG. 4, for each location for a grand total of 4200records, but five outlier signals were removed from each type leaving115 records a piece. The abbreviations are as follows: Baglow->Bag ofK'NEX dropped from 1.42 m (4.63 ft); Baghigh->Bag of K'NEX dropped from2.1 m (6.89 ft); Balllow->Basketball dropped from 1.42 m (4.63 ft);Ballhigh->Basketball dropped from 2.1 m (6.89 ft); Djump->Person Djumping (name left out for privacy); Jjump->Person J jumping;Wjump->person W jumping. The records were split into training (15records) and testing (100 records). The activity types are grouped byclassification, or general group (e.g. jump), and identification, or thespecific action (e.g. djump). The groupings are presented in Table 2,see FIG. 5.

Based on the assumption that the location of impact is known, perhapsusing the FEEL Algorithm, each sensor could have a specific classifierfor each location generated using the training dataset. The sensorswould operate in a multi-agent fashion as put forth in, Benjamin T.Davis et al. “Use of Wireless Smart Sensors for Detecting Human Fallsthrough Structural Vibrations”. In: Civil Engineering Topics. Ed. by TomProulx. Vol. 4. Springer, 2011, pp. 383-389, which is herebyincorporated by reference, allowing each sensor to make a decision basedon its own set of parameters embodied through the metrics.

In layer one, each agent would generate probabilities that one of sevenactivity types occurred for each record of the testing dataset. Next,the sensors would confer with one another and average theirprobabilities together by activity type. The highest probability wouldthen be considered the action classification.

Layer two would take the activity classification from layer one, andperform an additional analysis based solely on the identification typespresent in the activity classification. The same probabilistic procedureas layer one would be performed to determine the action identification.However, in other embodiments, the method may be flexible so anotherdeep learning, machine learning, or artificial intelligence algorithmcould be used for each layer or different metrics used in each layer.

All 127 unique combinations of metrics, i.e., all possible permutationsof the seven metrics used in the example, with metric order ignored, areattempted in an effort to determine the best combination. The secondlayer would only consider those combinations whose classification scoreswhere 90% or above, as anything lower is considered to be an ineffectivecombinations. In other words, layer one would use those metrics passingthe cut off and layer two would use all 127 metrics in comparison withthe layer one choices. Furthermore, a sensitivity analysis was performedto explore how sensitive machine-learning is to each of the metrics.

There were 22 metric combinations having 90% classification accuracy orabove on layer one, giving rise to 2794 possible combinations betweenthe two layers. Table 3, see FIG. 6, shows the accuracy results for thetop 22 combinations, with the best in each accuracy categoryhighlighted.

Layer two for the SVM only improved identification accuracy by 0.1% whencomparing to the best identification accuracy in each layer. Thecombination MA, SE, and ZCR shows up 17 times in the top 22 layer twocombinations, and in each instance, increases the identificationaccuracy. It even increased its own identification accuracy from layerone to take the top identification score in both layers. Table 4, seeFIG. 7, gives the top 22 identification scores for the second layer ofthe SVM.

There were 46 metric combinations having 90% classification accuracy orabove on layer one, giving rise to 2116 possible combinations betweenthe two layers. Table 5, see FIG. 8, shows the accuracy results for thetop 22 combinations, with the best in each accuracy categoryhighlighted.

Layer two for the MLR classifier showed some improvement inidentification accuracy, having a 5% increase over the best of layerone. Table 6, see FIG. 9, presents the scores for the top 22combinations in identification accuracy. The top identification score ishighlighted.

A deeper look past trying each metric combination separately leads to asensitivity analysis. Tree-Based Feature Selection was chosen toevaluate the sensitivity of the machine-learning to the various metricsexplored, see Pierre Geurts, Damien Ernst, and Louis Whenkel. “ExtremelyRandomized Trees”. In: Machine Learning 63 (2006), pp. 3-42. doi:10.1007/s10994-006-6226-1 and Cecille Freeman, Dana Kulic, and OtmanBasir. “Feature-Selected Tree-Based Classification”. In: IEEETransactions on Cybernetics 43.6 (December 2013), pp. 1990-2004. doi:10.1109/TSMCB.2012.2237394, which are hereby incorporated by reference,with the Python Scikit-Learn module providing the functionality, seeScikit-Learn. September 2015. url:http://scikit-learn.org/stable/index.html, which is also herebyincorporated by reference. Each event classification was looked atseparately to determine what metrics describe each action best, and thenall the event classifications were looked at together to get an overallset of metrics for human activity classification and identification.Importance factors were calculated to describe how well the metriccaptures variability of the dataset. Each location was consideredseparately, keeping in line with the assumption that the location isknown at the time of impact, and then the importance factors averagedtogether.

Interestingly, Signal Energy was in the top three of importance for allcases looked at, which shows in the results for both the MLR and SVMdiscussed earlier. Maximum Jerk and Maximum Amplitude appear in the topthree for three of the four cases, with Zero Crossing Rate and Forcefilling in the rest of the top three gaps. The results from attemptingeach unique combination match well with the results of the independentsensitivity study presented in FIG. 10. The metrics of more importancetend to show up in the top 22 more often than those of lower importance,showing good agreement with the aforementioned results.

The current disclosure also explored an unknown location trial. Thistrial used the same methodology as above, however, the location wasassumed to be unknown, thus adding another variable. The purpose was tosee if the technique could work in a more general sense, thus allowing asensor to be installed without having to be calibrated for location.Numerous benefits arise from this ability including ease of installationand sensor calibration, which would make activity monitoring usingstructural vibrations a more practical approach.

There were 16 metric combinations having 90% classification accuracy orabove on layer one, giving rise to 2032 possible combinations betweenthe two layers. Table 7, see FIG. 11, shows the accuracy results for thetop 20 combinations, with the best in each accuracy categoryhighlighted. Table 8 is shown at FIG. 12.

Identification accuracy increased by 2.1% when comparing the bestidentification accuracies between layer one and layer two. Only fourmetrics appear in the top twenty of layer two, and they also appear inlayer one predominantly: duration, maximum amplitude, signal energy, andzero crossing rate. Similar to the results for the known location trial,these metrics describe the shape of the impact which, in a way, is likethe fingerprint of the signal. The top scores are highlighted.

There were 22 metric combinations having 90% classification accuracy orabove on layer one, giving rise to 484 possible combinations between thetwo layers. Table 9, see FIG. 13, shows the accuracy results for the top20 combinations, with the best in each accuracy category highlighted.

Layer two for the MLR classifier showed some improvement inidentification accuracy, having a 11.0% increase over the best of layerone. Table 10, see FIG. 14, presents the scores for the top 20combinations in identification accuracy. The top identification score ishighlighted. FIG. 15 shows Table 11, which shows SVM Top 9Classification Metric Combination Scores for Layer One Assuming anUnknown Location and Sensor and FIG. 16 shows Table 12, which shows SVMTop 20 Classification Metric Combination Scores for Layer Two Assumingan Unknown Location and Sensor.

The metric combination of maximum amplitude and signal energy for layerone, and varying metric combinations for layer two show a veryconsistent trend of increasing identification accuracy from layer one byabout 10%. The same goes for using maximum amplitude, duration, andsignal energy for layer one, with the second layer consistentlyincreasing identification accuracy by about 13.7%.

The current disclosure also conducted an unknown location and sensortrial. This trial used the same methodology as the unknown locationtrial above, however, each sensor used the same machine learning modelthat was trained using all the training records from each location.There were 9 metric combinations having 90% classification accuracy orabove on layer one, giving rise to 1143 possible combinations betweenthe two layers.

There were 4 metric combinations having 90% classification accuracy orabove on layer one, giving rise to 508 possible combinations between thetwo layers. Table 13, see FIG. 17, shows the accuracy results for thetop combinations. Table 14, see FIG. 18, shows MLR Top 16 IdentificationMetric Combination Scores Layer Two Assuming an Unknown Location.

The following section provides an example of only using a signalanalysis/decomposition algorithm known to the practice and then usingits result coefficients/points straight with a machine learningalgorithm for determining an event. Autocorrelations are tools forfinding repeating patterns of a signal, even in the presence of noise.Actions, and objects, are distinguishable by the vibration pattern theyinduce, hence, autocorrelations are naturally included for their patternfinding properties, see Benjamin T. Davis et al. “Use of Wireless SmartSensors for Detecting Human Falls through Structural Vibrations”. In:Civil Engineering Topics. Ed. by Tom Proulx. Vol. 4. Springer, 2011, pp.383-389, which is hereby incorporated by reference. Equation 1A showsthe autocorrelation calculation of a discrete signal where t is thetime, N is the signal length, x is the signal, and τ is the timedisplacement (i.e. time lag), see Julius S. Bendat and Allan G. Piersol.Random Data: Analysis and Measurement Procedures. 3rd ed. John Wiley andSons, Inc., 2000, which is hereby incorporated by reference.

$\begin{matrix}{{R_{xx}(\tau)} = {\sum\limits_{t = 0}^{N}\; {{x(t)}{x\left( {t + \tau} \right)}}}} & \left( {1A} \right)\end{matrix}$

For the purposes of this research, the autocorrelations are normalizedusing Equation 2 where Rxx is the autocorrelation, μ is the mean of theautocorrelation, and σ is the standard deviation of the autocorrelation.This helps signals with like patterns to match more closely inmagnitude, which in turn aids in improving deep learning, machinelearning, or artificial intelligence's technique's accuracy technique'saccuracy, as they work not only on the order of values but themagnitudes as well.

$\begin{matrix}{R_{{xx},{norm}} = \frac{R_{xx} - \mu}{\sigma}} & (2)\end{matrix}$

This research used the Human Activity Benchmark dataset provided by theUniversity of South Carolina's Structural Dynamics and IntelligentInfrastructure (SDII) Laboratory, and developed by Diego Arocha, seeDiego Arocha. “Time Domain Methods to Study Human-StructureInteraction”. Master of Science. University of South Carolina, 2013,which is hereby incorporated by reference.

The experiments were performed in the second story office of theUniversity of South Carolina's (USC) Structures Laboratory, measuring777 cm (25.5 ft) by 638 cm (20.9 ft), that has reinforced concretefloors covered in vinyl tiles. PCB 333B50 accelerometers withsensitivity of 1000 mV/g were installed, with three being on the floornear the walls and one being in the center of the room. The sensors wereconnected to a NI CompactDAQ with a NI9234 module. Data was collected ata rate of 1651.7 Hz with 2 s windows. FIG. 3 shows the experimentallayout.

A total of 120 records are available for each activity type, describedin Table 1, see FIG. 4, for each location for a grand total of 4200records, but five outlier signals were removed from each type leaving115 records a piece. The records were split into training (15 records)and testing (100 records). The activity types are grouped byclassification, or general group (e.g. jump), and identification, or thespecific action (e.g. djump). The groupings are presented in Table 2,see FIG. 5.

The Normalized Autocorrelation (NA) of each signal for each sensor wastaken, assuming the location of each impact is known. Only half of theautocorrelation was considered (1653 coefficients) as the curve isnaturally symmetric about the midpoint. SVM and MLR techniques wereapplied considering various numbers of autocorrelation coefficientscounting from the origin onward, resulting in machine learning functionsfor each sensor for each location. The layering technique CDL was notused in conjunction with the NA as it would be redundant, and would notincrease accuracy. However, using other metrics in a second layer couldimprove accuracy as seen in the CDL example.

Overwhelmingly, MLR demonstrates superior capability to use normalizedautocorrelations for classification and identification, staying above96% and 72% regardless of the number of coefficients, respectively. TheSVM method, by comparison, begins with 80.1% accuracy in classificationand 50.4% accuracy in identification for ten coefficients considered,but deteriorates rapidly as the number of coefficients increases. Thisis due to how each technique “learns.” MLR attempts to make a logisticfunction that generates probabilities that are then used for classifyingnew events, whereas SVM creates hyperplanes that group provided data andmakes decisions based on similarity to “learned” patterns. Bynormalizing the autocorrelation coefficients, the magnitude becomes lessimportant and the shape of the curve takes on greater importance.

The number of coefficients needed for near perfect classificationaccuracy is less than that of Discrete Cosine Transforms (DCT), butstill larger the amount of values needed by the CDL using combination ofindividual metrics to get high accuracy. However, the autocorrelation ofa signal is quick computationally, and, given that using MLR with NAoffers 99.8% classification accuracy, the highest of any methodexplored, and competitively high accuracy in identification of 78.8%,the number of coefficients could considered unimportant.

Tables 15 and 16, see FIGS. 19-20, present the results for the MLR andSVM using NA, respectively, with the highest score highlighted for eachcategory.

The trial discussed above for Normalized Autocorrelations (knownlocation) was repeated, except this time it was assumed that thelocation of each impact was unknown. Results for MLR presented in Table17, see FIG. 21, and Table 18, see FIG. 22, for SVM. MLR performedsignificantly better than the SVM, tending to increase classificationand identification accuracy with increasing number of normalizedautocorrelation coefficients considered whereas the SVM decreased. Theclassification and identification accuracy of the SVM performed bestwith ten coefficients, yet, the MLR classifier did significantly betterwith the same number of coefficients having 13.5% better classificationaccuracy and 17.3% better identification accuracy.

The trial presented above for Normalized Autocorrelations (unknownlocation) was repeated with a slight change. All training records forthe five locations were used to generate one machine learning model.Next, each sensor used the same model to make predictions of what eachevent was. Table 19, see FIG. 23, shows MLR Results Using NA (unknownlocation and sensor). Table 20, see FIG. 24, shows: SVM Results Using NA(unknown location and sensor).

An example execution is described in the following. First, sensors areplaced through an area to monitor, attached to the structure anywherevibrations can be felt. Existing vibration data with associated actionlabels of what caused the vibrations is then used, in conjunction withany of the techniques mentioned within, to train a machine learningclassifier. Alternatively, new data can be collected using the installedsensors by performing actions within the structure, connecting theaction labels to the data, and then training the machine learningclassifier. The sensors then monitor vibrations within the structure.The incoming vibrations can be analyzed by the machine learningalgorithm in real time to identify what action caused the vibrations orrecorded data can later be analyzed after the fact.

In a further embodiment, sensor data may have undesirable signalcomponents, i.e. “noise”, removed before being provided to the deeplearning, machine learning, or artificial intelligence algorithm. Oneexample of this is through application of Spectral Subtraction withHalf-Wave Rectification methods. If one takes a signal comprised of adesired component and an undesired component of the form:

w _(signal) =w _(desired) +w _(noise)

where w_(signal) is the composite signal containing the desiredcomponent w_(desired) and the undesired noise component w_(noise). Ifw_(noise) is known, then w_(desired) may be obtained using SpectralSubtraction with Half-Wave Rectification as in

${{\hat{f}}_{desired}} = \left\{ \begin{matrix}{{{\hat{f}}_{signal}} > {{\hat{f}}_{noise}}} & {{{\hat{f}}_{signal}} - {{\hat{f}}_{noise}}} \\{else} & 0\end{matrix} \right.$

where {circumflex over (f)}_(desired) is the Discrete Fourier Transformof w_(desired), {circumflex over (f)}_(signal) is the Discrete FourierTransform of w_(signal), and {circumflex over (f)}_(noise) is theDiscrete Fourier Transform of w_(noise). The symbol ∥ ∥ indicatesabsolute value of the vector. The phase of {circumflex over(f)}_(signal) is then used to obtain w_(desired).

{circumflex over (f)} _(desired,i) =∥{circumflex over (f)}_(desired)∥_(i)×[cos(Φ_(signal,i))+sin(Φ_(signal,i))×j]

where Φ_(signal) is the is the phase of w_(signal) obtained from{circumflex over (f)}_(signal), and i is the index of the vector.

The method described above was applied to 540 individual recordings ofthree accelerometers' data totaling 2 seconds in length for tennis balldrops varying in height from 7 in to 35 in in 18 different locationsabove a floor. The accelerometers were placed randomly throughout the 10ft by 15 ft room. A dehumidifier was running, creating noise in thefloor in one corner, near two of the accelerometers. In an example of apreferred embodiment, the one second of signal just before the time ofthe tennis ball impact was used as w_(noise). Note that any length ofw_(noise) signal may be used to clean any length of w_(signal), the twodo not have to be the same length. FIGS. 25, 26 and 27 show examples ofnoise just before the signal of interest, the signal of interest, andthe desired signal after applying the aforementioned method for each ofthe three accelerometers.

While the present subject matter has been described in detail withrespect to specific exemplary embodiments and methods thereof, it willbe appreciated that those skilled in the art, upon attaining anunderstanding of the foregoing may readily produce alterations to,variations of, and equivalents to such embodiments. Accordingly, thescope of the present disclosure is by way of example rather than by wayof limitation, and the subject disclosure does not preclude inclusion ofsuch modifications, variations and/or additions to the present subjectmatter as would be readily apparent to one of ordinary skill in the artusing the teachings disclosed herein.

What is claimed is:
 1. A system for classifying actions based onvibrations comprising: at least one sensor; at least one data collectorthat receives information from the at least one sensor; a processoranalyzing a vibration signal obtained from the information received fromthe at least one sensor, wherein the processor determines time domaincomponents and/or frequency domain components of the vibration signal;and a classifier that employs at least one algorithm to analyze the timedomain components and/or frequency domain components of the vibrationsignal, wherein the classifier associates the time domain componentsand/or frequency domain components of the vibration signal with a knownaction.
 2. The system of claim 1, wherein the known action comprises aform of human activity.
 3. The system of claim 1, wherein the knownaction comprises a form of activity other than human activity.
 4. Thesystem of claim 1, wherein the at least one sensor comprises a vibrationmeasuring sensor.
 5. The system of claim 1, wherein the time domaincomponent comprises amplitude, zero-crossing rate, and/or duration. 6.The system of claim 1, wherein the frequency domain components comprisesFourier transform, discrete cosine transform, and/or power density. 7.The system of claim 1, wherein the classifier provides a probabilityassessment that correlates the vibration signal to a known action withina predefined confidence level.
 8. The system of claim 1, wherein atleast one machine learning or artificial intelligence associates thetime domain components and/or frequency domain components of thevibration signal with the known action.
 9. The system of claim 1,wherein sound or vibration components are processed from the vibrationsignal prior to the at least one algorithm being used to analyze thetime domain components and/or frequency domain components of thevibration signal.
 10. A method for categorizing actions based onvibrations comprising: detecting at least one vibration; converting theat least one vibration into information; obtaining at least onevibration signal from the information; determining time domaincomponents and/or frequency domain components of the vibration signal;analyzing the time domain components and/or frequency domain componentsof the vibration signal; and associating the time domain componentsand/or frequency domain components of the vibration signal with a knownaction.
 11. The system of claim 10, wherein the known action comprises aform of human activity.
 12. The system of claim 10, wherein the knownaction comprises a form of activity other than human activity.
 13. Thesystem of claim 10, wherein the at least one sensor comprises avibration measuring sensor.
 14. The system of claim 10, wherein the timedomain component comprises maximum amplitude, zero-crossing rate, and/orduration.
 15. The system of claim 10, wherein the frequency domaincomponents comprises Fourier transform, discrete cosine transform,and/or power density.
 16. The system of claim 10, wherein the classifierprovides a probability assessment that correlates the vibration signalto a known action within a predefined confidence level.
 17. The systemof claim 10, wherein at least one machine learning, deep learning, orartificial intelligence associates the time domain components and/orfrequency domain components of the vibration signal with the knownaction.
 18. The system of claim 10, wherein sound or vibrationcomponents are processed from the vibration signal prior to the at leastone algorithm being used to analyze the time domain components and/orfrequency domain components of the vibration signal.